The increasing popularity of mobile communications has placed a tremendous demand on the scarce radio resources of cellular communication networks. To efficiently utilize these valuable resources, radio frequencies in Time Division Multiple Access (TDMA) cellular systems such as GSM/EDGE, are being reused with closer proximity than ever. As a result, mutual interference among users occupying the same radio channel has become a major source of signal disturbance. The ability to suppress co-channel interference has become increasingly important for mobile receivers in cellular systems with tight reuse.
Multi-branch diversity or array processing is a class of commonly used techniques for suppressing interference, in which multiple versions of the same transmitted signal are produced and processed jointly in the receiver in order to cancel one or more interfering signal(s). The different signal versions may be obtained by using multiple receiving antennas, by sampling the received signal over the baud rate of transmission (i.e., oversampling), by separating in-phase (I) and quadrature-phase (Q) of the signal, or by combinations of these. The method of separating in-phase (I) and quadrature-phase (Q) of the signal is commonly referred to as the single-antenna-interference cancellation (SAIC) method and has recently received much attention in GERAN standardization.
In conventional array processing, the interference is typically modeled as temporally (across time) and/or spatially (across different signal versions) colored noise. By performing proper spatial and/or temporal noise whitening, the interference can be suppressed substantially. Such whitening operation may be performed before or during demodulation/equalization.
The demodulator or equalizer in a mobile receiver typically requires an estimate of the channel response of the received signal. In addition, the demodulator or equalizer must also be able to synchronize to the beginning of a data burst in order to begin demodulation. The synchronization and channel estimation process is typically done over a sequence of training symbols in each data burst that is known to the receiver. When spatial/temporal whitening is performed during equalization or demodulation, the operating carrier-to-interference power ratio (C/I) can be changed so drastically that the ordinary method of synchronization and channel estimation, such as the least-squares (LS) method, can no longer produce an accurate synchronization position or channel estimate. As a result, the reliability of synchronization and channel estimation becomes a bottleneck of the overall receiver performance.
One known way of improving synchronization and quality of channel estimation in a multi-branch receiver is to re-synchronize and re-estimate a whitened channel response over a sequence of training symbols after applying a spatial-temporal whitening filter to the received signal. Such a whitening filter can be computed using the well-known Whittle-Wiggins-Robinson Algorithm (WWRA) (or sometimes referred to as the generalized Levinson-Durbin algorithm) based on the residual signal generated by a simple channel estimator such as the least-squares channel estimator. Alternatively, the whitened channel response and the whitening filter coefficients may be jointly estimated using the known indirect generalized least-squares (iGLS) algorithm. Note that the whitening filter coefficients computed using either the WWRA or the iGLS algorithm are square matrices.
Conventional methods of channel estimation with spatial-temporal whitening focus on the estimation of whitened channel response. These methods do not exploit the convolutional structure of the whitened channel, and thus attempt to estimate more unknown parameters than is needed. As a result, the quality of the channel estimate suffers. What is needed in the art is an improved method and device in a radio receiver for generating synchronization and channel estimation information that overcomes such deficiencies.